Artificial intelligence is often discussed in broad terms across the insurance industry. In practice, many of the most meaningful improvements are happening in specific operational moments inside the insurance workflow. One of those moments is submission intake. From the moment a submission enters the quoting process, insurers and MGAs must quickly determine whether a risk aligns with underwriting appetite, whether sufficient data is available to evaluate the risk, and whether the opportunity should move forward in the pipeline. When submissions arrive incomplete or misaligned, they create friction across quoting and underwriting workflows. In a recent commentary published in Insurance Innovation Reporter, Fenris CEO Jennifer Linton explores how insurers are using AI and external data enrichment to improve this process and reduce operational bottlenecks.  __

Excerpt from Insurance Innovation Reporter

AI appears in headlines across many industries. In property/casualty insurance, however, its impact is showing up in a series of practical operational improvements, including faster quoting, sharper appetite screening, lower acquisition costs, and more profitable portfolios. Across the industry, insurers are already using predictive models to qualify submissions in milliseconds and flag misaligned risks before underwriters ever open a file. These gains often come from combining third-party data enrichment with behavioral scoring in ways that streamline distribution without requiring a core-system overhaul. For insurers and managing general agencies (MGAs) with decades invested in legacy IT, that distinction matters.

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Why This Matters for Insurers

For insurers and MGAs, submission quality has a direct impact on operational efficiency and portfolio performance. When submissions arrive with limited data or fall outside underwriting appetite, they create unnecessary work for underwriting teams and slow the quote-to-bind process. At scale, this friction increases acquisition costs and reduces distribution efficiency. This is why many insurers are beginning to apply predictive scoring and external data enrichment earlier in the workflow. By analyzing additional risk attributes and evaluating submission quality in milliseconds, insurers can identify well-aligned opportunities sooner and reduce time spent reviewing risks that are unlikely to bind. In practice, these capabilities are often deployed through API-based integrations that enrich submissions and generate predictive signals at intake, helping distribution and underwriting teams focus on the most promising opportunities. Rather than replacing core systems, these approaches typically operate alongside existing technology stacks, allowing insurers to introduce intelligence incrementally without undertaking large infrastructure transformation projects.

Read the Full Article

Jennifer Linton’s full commentary in Insurance Innovation Reporter explores how insurers are using predictive models and external data enrichment to reduce submission friction and improve underwriting efficiency across distribution.

Read the full article here:
https://iireporter.com/ai-reduces-submission-friction-for-pc-insurers